HealthDial: A No-Code LLM-Assisted Dialogue Authoring Tool for Healthcare Virtual Agents
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arXiv
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| Format: | Preprint |
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2025
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| author | Nouraei, Farnaz Yong, Zhuorui Bickmore, Timothy |
| author_facet | Nouraei, Farnaz Yong, Zhuorui Bickmore, Timothy |
| contents | We introduce HealthDial, a dialogue authoring tool that helps healthcare providers and educators create virtual agents that deliver health education and counseling to patients over multiple conversations. HealthDial leverages large language models (LLMs) to automatically create an initial session-based plan and conversations for each session using text-based patient health education materials as input. Authored dialogue is output in the form of finite state machines for virtual agent delivery so that all content can be validated and no unsafe advice is provided resulting from LLM hallucinations. LLM-drafted dialogue structure and language can be edited by the author in a no-code user interface to ensure validity and optimize clarity and impact. We conducted a feasibility and usability study with counselors and students to test our approach with an authoring task for cancer screening education. Participants used HealthDial and then tested their resulting dialogue by interacting with a 3D-animated virtual agent delivering the dialogue. Through participants' evaluations of the task experience and final dialogues, we show that HealthDial provides a promising first step for counselors to ensure full coverage of their health education materials, while creating understandable and actionable virtual agent dialogue with patients. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_15898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HealthDial: A No-Code LLM-Assisted Dialogue Authoring Tool for Healthcare Virtual Agents Nouraei, Farnaz Yong, Zhuorui Bickmore, Timothy Human-Computer Interaction Computation and Language Computers and Society 68T42 I.2.1; J.3 We introduce HealthDial, a dialogue authoring tool that helps healthcare providers and educators create virtual agents that deliver health education and counseling to patients over multiple conversations. HealthDial leverages large language models (LLMs) to automatically create an initial session-based plan and conversations for each session using text-based patient health education materials as input. Authored dialogue is output in the form of finite state machines for virtual agent delivery so that all content can be validated and no unsafe advice is provided resulting from LLM hallucinations. LLM-drafted dialogue structure and language can be edited by the author in a no-code user interface to ensure validity and optimize clarity and impact. We conducted a feasibility and usability study with counselors and students to test our approach with an authoring task for cancer screening education. Participants used HealthDial and then tested their resulting dialogue by interacting with a 3D-animated virtual agent delivering the dialogue. Through participants' evaluations of the task experience and final dialogues, we show that HealthDial provides a promising first step for counselors to ensure full coverage of their health education materials, while creating understandable and actionable virtual agent dialogue with patients. |
| title | HealthDial: A No-Code LLM-Assisted Dialogue Authoring Tool for Healthcare Virtual Agents |
| topic | Human-Computer Interaction Computation and Language Computers and Society 68T42 I.2.1; J.3 |
| url | https://arxiv.org/abs/2510.15898 |